GRAM-DTI: adaptive multimodal representation learning for drug target interaction prediction

• URL: http://arxiv.org/abs/2509.21971v2
• Date: Tue, 04 Nov 2025 10:56:24 GMT
• Title: GRAM-DTI: adaptive multimodal representation learning for drug target interaction prediction
• Authors: Feng Jiang, Amina Mollaysa, Hehuan Ma, Tommaso Mansi, Junzhou Huang, Mangal Prakash, Rui Liao,
• Abstract summary: GRAMDTI is a pretraining framework that integrates multimodal molecular and protein inputs into unified representations.<n>GRAMDTI extends volume based contrastive learning to four modalities, capturing higher-order semantic alignment.<n>Our results highlight the benefits of higher order multimodal alignment, adaptive modality utilization, and auxiliary supervision for robust and generalizable DTI prediction.
• Score: 25.25496268607753
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Drug target interaction (DTI) prediction is a cornerstone of computational drug discovery, enabling rational design, repurposing, and mechanistic insights. While deep learning has advanced DTI modeling, existing approaches primarily rely on SMILES protein pairs and fail to exploit the rich multimodal information available for small molecules and proteins. We introduce GRAMDTI, a pretraining framework that integrates multimodal molecular and protein inputs into unified representations. GRAMDTI extends volume based contrastive learning to four modalities, capturing higher-order semantic alignment beyond conventional pairwise approaches. To handle modality informativeness, we propose adaptive modality dropout, dynamically regulating each modality's contribution during pre-training. Additionally, IC50 activity measurements, when available, are incorporated as weak supervision to ground representations in biologically meaningful interaction strengths. Experiments on four publicly available datasets demonstrate that GRAMDTI consistently outperforms state of the art baselines. Our results highlight the benefits of higher order multimodal alignment, adaptive modality utilization, and auxiliary supervision for robust and generalizable DTI prediction.

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